This is a joint PhD studentship between Manchester Metropolitan and Image Metrics Ltd, funded by The Royal Society. This project will improve the realism of facial appearance try-on technology by developing a novel and light-weight solution for real-time inpainting. This research will investigate deep learning architectures for various augmented reality tasks.
Virtual try-on technology assists consumer in making purchase decision when shop online. With the popularity of mobile devices, virtual try-on for faces has been widely used for cosmetic and gaming. In medical practices for face reconstruction surgery, there is a growing need to be able to find the realistic appearance for people with face defects. In addition, the ability to predict a face with occlusion can improve the face recognition rate in security application. This project proposes a new technique that can inpaint the missing face region with realistic appearance. Recent work shows the ability to inpaint face regions however these methods only work on small images and take a long processing time. This project will design a solution using a machine intelligence method that work efficiently on mobile devices. The solution will be tested and deployed in collaboration with Image Metrics Ltd in real-world applications.
The PhD candidate will receive professional development training from the University, the industry partner and attend external training. To ensure the candidate has exposure to commercial setting, s/he will spend 60% of their time at Manchester Metropolitan University and 40% at Image Metrics Ltd (http://image-metrics.com). The joint supervision will broaden the perspective on the research impact and enrich the student experience as s/he gains a wider understanding of applied research and different training environments. The PhD student will have access to training, facilities and expertise in both organisations, which is very valuable particularly in enhancing their employability, ideally becoming a leader in her/his field. With such setting, the student will benefit from different algorithm/software development with an applied or translational dimension.
To apply, please follow: https://www2.mmu.ac.uk/research/research-study/scholarships/detail/scieng-mhy-2018-1-the-royal-society-fully-funded-phd-studentship-deep-learning-to-improve-augmented-reality-application.php
For informal discussion, email: M.Yap at mmu.ac.uk<mailto:M.Yap at mmu.ac.uk>
Many thanks
Best regards
Moi Hoon
Dr. Moi Hoon Yap
Royal Society Industry Fellow, Image Metrics Ltd
Reader, Manchester Metropolitan University
Address:
John Dalton Building (E129) | Chester Street | Manchester | M1 5GD
Telephone: (+44) 0161 247 1503 | Facsimile: (+44) 0161 247 6840
Website: http://www2.docm.mmu.ac.uk/STAFF/M.Yap/
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